适用于简单数值输入输出的Keras/TensorFlow激活函数及参数选型

Hey Andy, let's walk through your problem clearly—you're working on a regression task with specific input/output bounds and a large, scalable dataset, so we can tailor every recommendation to fit your needs perfectly.

First, let's tackle the activation function since that's your biggest pain point:

• Output layer: Your target range is -5.0 < x < 5.0, so two solid options here:
  1. Linear activation (linear): The simplest choice, no transformation—great if you trust your model to naturally learn the output bounds without hard constraints.
  2. Scaled tanh: Use 5 * tanh(x) instead of raw tanh. Since tanh outputs values between (-1, 1), multiplying by 5 directly maps it to your desired (-5, 5) range. This is useful if you want to force predictions to stay within your bounds, especially if you see model outputs drifting outside the range during training.
• Hidden layers: Stick with tried-and-true options for large datasets:
  • ReLU or LeakyReLU: ReLU is fast and avoids vanishing gradients, while LeakyReLU fixes the "dead neuron" problem where some units stop activating entirely. Both work great for large-scale training.
  • GELU: If you have the compute power, GELU (used in Transformers) can capture more complex non-linearities, but it's slightly slower than ReLU—worth testing if your data has tricky patterns.

Your input is 98 floating-point values, so the best choice depends on the structure of your data:

• MLP (Multi-Layer Perceptron): Go here first if your 98 features are structured (non-sequential, non-spatial). It's simple, fast to train, and works incredibly well for large tabular/feature-based datasets. No need to overcomplicate with more complex architectures unless you have specific data patterns.
• LSTM/GRU: Use this only if your 98 values are a time-series sequence (e.g., 98 time steps of sensor data). LSTMs handle temporal dependencies better than MLPs, but they're slower to train. GRUs are a lighter alternative to LSTMs with similar performance.
• CNN: Only consider this if your 98 features have a spatial structure (e.g., they're a flattened 7x14 image). CNNs excel at extracting local spatial patterns, but it's overkill if your data is just unstructured features.

With a 10GB dataset, overfit risk is lower, but Dropout still helps generalize:

• MLP: Add Dropout layers after each hidden layer with a rate of 0.2–0.5. Start at 0.3—if your validation loss is much higher than training loss, bump it up; if training loss is high, lower it.
• LSTM/GRU: Use recurrent dropout (rate 0.1–0.3) within the recurrent layer, plus a regular Dropout layer (0.2–0.4) after the LSTM/GRU output. Avoid setting recurrent dropout too high—it can slow training and destabilize gradients.

Keep it simple to avoid unnecessary complexity:

• MLP: Start with 2–4 hidden layers. A good starting point:
  • Layer 1: 128–256 neurons (1–2x your input size of 98)
  • Layer 2: 64–128 neurons
  • Layer 3 (optional): 32–64 neurons
    Gradually shrink the number of neurons as you go deeper to compress features.
• LSTM/GRU: 1–2 LSTM/GRU layers with 64–128 neurons each, followed by 1–2 small fully connected layers (32–64 neurons) to map to the single output.

Since your dataset is large and scalable, batch size should balance speed and gradient stability:

• For GPU training: Start with 256–1024. If your GPU has plenty of VRAM (16GB+), go with 512 or 1024—larger batches speed up training and smooth out gradient updates. If you hit memory limits, drop down to 256.
• For CPU training: Stick to smaller batches (32–128) to avoid excessive memory usage, but note that CPU training will be slow for a 10GB dataset—GPU is highly recommended.

• AdamW: This is the top recommendation. It's an improved version of Adam with weight decay, which helps prevent overfitting on large datasets. Start with a learning rate of 1e-4 to 1e-3. Pair it with a learning rate scheduler like ReduceLROnPlateau to lower the rate when validation loss stops improving.
• SGD with Momentum: If you prefer a more traditional optimizer, use SGD with momentum=0.9. It can sometimes generalize better than AdamW, but it converges slower—you'll need a higher initial learning rate (1e-3 to 1e-2) and patience.

• Input Scaling: Your inputs are already in (-5,5), but scaling them to (-1,1) (divide by 5) can help hidden layer activations work more efficiently. It's optional but worth testing.
• Loss Function: Use MSE (Mean Squared Error) for standard regression—penalizes large errors more heavily. If your dataset has lots of outliers, switch to MAE (Mean Absolute Error) for more robustness.

内容的提问来源于stack exchange，提问作者AndyPrince